This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification purposes. Experiments performed on two different challenging datasets demonstrate that the proposed strategy outperforms the stateof- the-art approaches from which our solution originates.

This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification purposes. Experiments performed on two different challenging datasets demonstrate that the proposed strategy outperforms the stateof- the-art approaches from which our solution originates.

Aligning Bags of Shape Contexts for Blurred Shape Model Based Symbol Classification

BATTIATO, SEBASTIANO;FARINELLA, GIOVANNI MARIA;
2012-01-01

Abstract

This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification purposes. Experiments performed on two different challenging datasets demonstrate that the proposed strategy outperforms the stateof- the-art approaches from which our solution originates.
978-4-9906441-1-6
This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification purposes. Experiments performed on two different challenging datasets demonstrate that the proposed strategy outperforms the stateof- the-art approaches from which our solution originates.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/92151
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